Spherical Warping Layer (SWL) The extracted feature maps of all views are then used to build a cost volume at multiple depth hypothesis planes for cost matching.
An essential step of cost volume construction is to determine the coordinate mapping, which is reflected as disparity, that warps reference view to the target view.
Unlike perspective images where the disparity is proportional to the inverse depth [1]}, [2]}, [3]}, disparity of 360 stereo pairs is related to both inverse depth and spherical latitudes.
Our SWL performs direct depth sampling instead of disparity sampling which is commonly used in perspective image pair stereo matching. Compared with the learnable shifting filters proposed in [4]}, our SWL makes the disparity computation adjustable to the pixel latitudinal value, without introducing additional computation overhead in training.
Moreover, SWL directly samples on the absolute depth domain, thereby enabling horizontal 360 stereo (which has both horizontal and vertical disparity, more details in the Appendix), the adaptation of 360 multi-view, and the usage of cascade mechanism.
